3D tensors expect 2 values for padding Torch - pytorch

Question

I have succesfully trained and tested my model using this tutorial, and I want to test my model using single images. Here is my code
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader,Dataset
import matplotlib.pyplot as plt
import torchvision.utils
import numpy as np
import random
from PIL import Image
import torch
from torch.autograd import Variable
import PIL.ImageOps
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import os
class Config():
training_dir = "./data/faces/training/"
testing_dir = "./data/faces/testing/"
train_batch_size = 80
train_number_epochs = 100
class SiameseNetworkDataset(Dataset):
...
class SiameseNetwork(nn.Module):
...
class ContrastiveLoss(torch.nn.Module):
...
if __name__=='__main__':
net = SiameseNetwork().cuda()
net.load_state_dict(torch.load("model.pt"))
img0 = Image.open(os.path.join('data', 'faces', 'testing', 's5', '2.png'))
img1 = Image.open(os.path.join('data', 'faces', 'testing', 's5', '1.png'))
img0 = img0.convert("L")
img1 = img1.convert("L")
img0 = PIL.ImageOps.invert(img0)
img1 = PIL.ImageOps.invert(img1)
transform=transforms.Compose([transforms.Resize((100,100)),
transforms.ToTensor()
])
img0 = transform(img0)
img1 = transform(img1)
img0 = img0.cuda()
img1 = img1.cuda()
output1,output2 = net(Variable(img0).cuda(),Variable(img1).cuda()) //The error occurred here
euclidean_distance = F.pairwise_distance(output1, output2)
print(euclidean_distance.cpu().data.numpy())
And I have the following error: AssertionError: 3D tensors expect 2 values for padding. I can't understand, what is wrong, because I use the same preprocessing, which is used in SiameseNetworkDataset